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Record W1979992624 · doi:10.7202/1013959ar

BBC English with an Accent: “African” and “Asian” Accents and the Translation of Culture in British Broadcasting

2013· article· en· W1979992624 on OpenAlexvenueno aff
Kenn Nakata Steffensen

Bibliographic record

VenueMeta Journal des traducteurs · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsStress (linguistics)LinguisticsIndigenousColonialismSpeech communitySociologyHistory

Abstract

fetched live from OpenAlex

Foreign accents acted by Anglophone actors are a ubiquitous but politically and theoretically problematic feature of many audiovisual productions in the English-speaking world. This paper investigates the use of Tswana and Japanese accents in two BBC productions as acts of audiovisual translation (AVT) which are illustrative of a more general problematic of Western representations of non-Western languages and cultures. It argues that the phonological features of speech, which are classified as accents, divide the community of native speakers into different social groups and that they create and maintain boundaries between native and non-native speakers. Language discrimination is recognised by the BBC as a problem in relation to its domestic audience and the Corporation actively attempts to become more inclusive and representative of British society by broadcasting non-standard accents. On the other hand, when representing foreign, and especially post-colonial and non-Western languages and cultures, accent is used to define the boundary between the native English-speaking community and its outside . Accents are used to represent and translate the outside in stereotyping ways that tend towards racialisation and towards actors using generic “Southern African” and “East Asian” accents that bear little resemblance to the actual phonological profile of native speakers of Tswana and Japanese.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.355
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2013
Admission routes1
Has abstractyes

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